{"id":"https://openalex.org/W7160447127","doi":"https://doi.org/10.48550/arxiv.2605.02917","title":"PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL","display_name":"PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7160447127","doi":"https://doi.org/10.48550/arxiv.2605.02917"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02917","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.02917","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135485408","display_name":"Sheng Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wong, Sheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101839642","display_name":"Ravi Shankar","orcid":"https://orcid.org/0000-0003-2106-2633"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shankar, Ravi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009106997","display_name":"Beth Albert","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Albert, Beth","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135468465","display_name":"Hao Fei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fei, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135481874","display_name":"Lin Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086944354","display_name":"Imane Ben M\u2019Barek","orcid":"https://orcid.org/0000-0002-2495-0292"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M'Barek, Imane Ben","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135515886","display_name":"Manu Vatish","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vatish, Manu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5008942107","display_name":"Gabriel Davis Jones","orcid":"https://orcid.org/0000-0003-4070-4814"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jones, Gabriel Davis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.7088000178337097,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11184","display_name":"Neonatal and fetal brain pathology","score":0.7088000178337097,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12552","display_name":"Fetal and Pediatric Neurological Disorders","score":0.04879999905824661,"subfield":{"id":"https://openalex.org/subfields/2735","display_name":"Pediatrics, Perinatology and Child Health"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11290","display_name":"Preterm Birth and Chorioamnionitis","score":0.042399998754262924,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.6154000163078308},{"id":"https://openalex.org/keywords/cardiotocography","display_name":"Cardiotocography","score":0.5300999879837036},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5030999779701233},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5026999711990356},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.4666000008583069},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45579999685287476},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3691999912261963},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.3605000078678131}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6362000107765198},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6154000163078308},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5752000212669373},{"id":"https://openalex.org/C2776046940","wikidata":"https://www.wikidata.org/wiki/Q886292","display_name":"Cardiotocography","level":4,"score":0.5300999879837036},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5026999711990356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4957999885082245},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3774999976158142},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3605000078678131},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34630000591278076},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C147494362","wikidata":"https://www.wikidata.org/wiki/Q2078905","display_name":"Troubleshooting","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.31119999289512634},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C187029079","wikidata":"https://www.wikidata.org/wiki/Q958679","display_name":"Cognitive reframing","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29499998688697815},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27720001339912415},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02917","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.02917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02917","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Supervised":[0],"deep":[1],"learning":[2],"models":[3],"for":[4,43,53,204],"automated":[5],"CTG":[6,44,54,151,205],"analysis":[7],"are":[8,121],"typically":[9],"constrained":[10],"by":[11],"narrowly":[12],"curated":[13],"labelled":[14,190],"datasets":[15],"and":[16,41,88,117,156,165],"limited":[17],"patient":[18,115],"cohorts,":[19],"leaving":[20],"substantial":[21],"volumes":[22],"of":[23],"physiologically":[24],"informative":[25],"clinical":[26,85,111,134],"recordings":[27,59],"untapped.":[28],"To":[29,192],"address":[30],"this":[31,195],"limitation,":[32],"we":[33],"propose":[34],"Physiology-aware":[35],"Representation":[36],"Learning":[37],"via":[38],"Integrated":[39],"Self-supervision":[40],"Metadata":[42],"(PRISM-CTG),":[45],"a":[46,69,96],"clinically":[47,142],"grounded":[48],"self-supervised":[49,71],"foundation":[50],"model":[51],"(FM)":[52],"that":[55,73,140,160,206],"leverages":[56],"large-scale":[57,202],"unlabelled":[58],"to":[60,183,200],"learn":[61],"transferable":[62],"domain-level":[63,208],"representations.":[64,209],"PRISM-CTG":[65,161,169],"is":[66,93,196],"pretrained":[67],"using":[68],"multi-view":[70],"framework":[72],"jointly":[74],"optimises":[75],"3":[76],"complementary":[77],"pretext":[78],"objectives:":[79],"random-projected":[80],"guided":[81],"masked":[82],"signal":[83],"reconstruction,":[84],"variable":[86],"prediction,":[87],"feature":[89],"classification.":[90],"Each":[91],"objective":[92],"associated":[94],"with":[95],"dedicated":[97],"task-specific":[98],"token,":[99],"enabling":[100],"specialised":[101],"representation":[102,144],"learning,":[103],"while":[104,179],"controlled":[105],"cross-attention":[106],"facilitates":[107],"information":[108,135],"exchange":[109],"across":[110,148],"context.":[112],"By":[113],"reframing":[114],"metadata":[116],"domain":[118],"knowledge,":[119,194],"which":[120],"often":[122],"underutilised":[123],"in":[124,153],"conventional":[125],"training":[126],"as":[127],"prediction":[128],"targets,":[129],"Prism-CTG":[130],"transforms":[131],"readily":[132],"available":[133],"into":[136],"additional":[137],"supervisory":[138],"targets":[139],"guide":[141],"meaningful":[143],"learning.":[145],"Extensive":[146],"experiments":[147],"7":[149],"downstream":[150],"tasks":[152],"both":[154],"antepartum":[155],"intrapartum":[157],"domains":[158],"demonstrated":[159,170],"consistently":[162],"outperforms":[163],"in-domain":[164],"SSL":[166],"baselines.":[167],"Notably,":[168],"strong":[171],"generalisation":[172],"under":[173],"external":[174],"validation":[175],"on":[176,186],"2":[177],"datasets,":[178],"achieving":[180],"comparable":[181],"performance":[182],"studies":[184],"trained":[185],"substantially":[187],"larger,":[188],"privately":[189],"datasets.":[191],"our":[193],"the":[197],"first":[198],"study":[199],"introduce":[201],"FM":[203],"learns":[207]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-07T00:00:00"}